templates: Add neo4j semantic layer with ollama template (#17192)

A template with JSON-based agent using Mixtral via Ollama.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

templates/neo4j-semantic-ollama/neo4j_semantic_ollama/utils.py

@@ -0,0 +1,84 @@
+from typing import Dict, List
+
+from langchain_community.graphs import Neo4jGraph
+
+graph = Neo4jGraph()
+
+
+def get_user_id() -> int:
+    """
+    Placeholder for a function that would normally retrieve
+    a user's ID
+    """
+    return 1
+
+
+def remove_lucene_chars(text: str) -> str:
+    """Remove Lucene special characters"""
+    special_chars = [
+        "+",
+        "-",
+        "&",
+        "|",
+        "!",
+        "(",
+        ")",
+        "{",
+        "}",
+        "[",
+        "]",
+        "^",
+        '"',
+        "~",
+        "*",
+        "?",
+        ":",
+        "\\",
+    ]
+    for char in special_chars:
+        if char in text:
+            text = text.replace(char, " ")
+    return text.strip()
+
+
+def generate_full_text_query(input: str) -> str:
+    """
+    Generate a full-text search query for a given input string.
+
+    This function constructs a query string suitable for a full-text search.
+    It processes the input string by splitting it into words and appending a
+    similarity threshold (~0.8) to each word, then combines them using the AND
+    operator. Useful for mapping movies and people from user questions
+    to database values, and allows for some misspelings.
+    """
+    full_text_query = ""
+    words = [el for el in remove_lucene_chars(input).split() if el]
+    for word in words[:-1]:
+        full_text_query += f" {word}~0.8 AND"
+    full_text_query += f" {words[-1]}~0.8"
+    return full_text_query.strip()
+
+
+candidate_query = """
+CALL db.index.fulltext.queryNodes($index, $fulltextQuery, {limit: $limit})
+YIELD node
+RETURN coalesce(node.name, node.title) AS candidate,
+       [el in labels(node) WHERE el IN ['Person', 'Movie'] | el][0] AS label
+"""
+
+
+def get_candidates(input: str, type: str, limit: int = 3) -> List[Dict[str, str]]:
+    """
+    Retrieve a list of candidate entities from database based on the input string.
+
+    This function queries the Neo4j database using a full-text search. It takes the
+    input string, generates a full-text query, and executes this query against the
+    specified index in the database. The function returns a list of candidates
+    matching the query, with each candidate being a dictionary containing their name
+    (or title) and label (either 'Person' or 'Movie').
+    """
+    ft_query = generate_full_text_query(input)
+    candidates = graph.query(
+        candidate_query, {"fulltextQuery": ft_query, "index": type, "limit": limit}
+    )
+    return candidates